def wproject(vis, npixel_advice, cell_advice, channel, results_dir):
"""Do w-projected imaging of visibility data.
Args:
vis (obj): ARL visibility data.
npixel_advice (float): number of pixels in output image.
cell_advice (float): cellsize in output image.
channel (int): channel number to be imaged (affects output filename).
results_dir (str): directory to save results.
Returns:
dirty: dirty image.
psf: image of psf.
"""
try:
vis_slices = len(np.unique(vis.time))
print("There are %d timeslices" % vis_slices)
# Obtain advice on w-proj parameters:
advice = advise_wide_field(vis)
# Create a model image:
model = create_image_from_visibility(vis, cellsize=cell_advice,
npixel=npixel_advice,
polarisation_frame=PolarisationFrame('stokesIQUV'))
# Weight the visibilities:
vis, _, _ = weight_visibility(vis, model)
# Create a dirty image:
dirty, sumwt = create_invert_graph([vis], model, kernel='wprojection',
wstep=advice['w_sampling_primary_beam'],
oversampling=2).compute()
# Create the psf:
psf, sumwt = create_invert_graph([vis], model, dopsf=True, kernel='wprojection',
wstep=advice['w_sampling_primary_beam'],
oversampling=2).compute()
# Save to disk:
export_image_to_fits(dirty, '%s/imaging_dirty_WProj-%s.fits'
% (results_dir, channel))
export_image_to_fits(psf, '%s/imaging_psf_WProj-%s.fits'
% (results_dir, channel))
except:
print("Unexpected error:", sys.exc_info()[0])
raise
return dirty, psf
def test_invert_graph(self):
dirty_graph = create_invert_graph(self.vis_graph_list, self.model_graph,
dopsf=False, normalize=True)
if self.compute:
dirty = dirty_graph.compute()
export_image_to_fits(dirty[0], '%s/test_imaging_invert_graph_dirty.fits' % (self.results_dir))
qa = qa_image(dirty[0])
assert numpy.abs(qa.data['max'] - 101.7) < 1.0, str(qa)
assert numpy.abs(qa.data['min'] + 3.5) < 1.0, str(qa)